coreweave-core-workflow-b

name	coreweave-core-workflow-b
description	Run distributed GPU training jobs on CoreWeave with multi-node PyTorch. Use when training models across multiple GPUs, setting up distributed training, or running fine-tuning jobs on CoreWeave H100 clusters. Trigger with phrases like "coreweave training", "coreweave multi-gpu", "distributed training coreweave", "fine-tune on coreweave".
allowed-tools	Read, Write, Edit, Bash(kubectl:*), Grep
version	1.11.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","gpu-cloud","kubernetes","inference","coreweave"]
compatibility	Designed for Claude Code

CoreWeave Core Workflow: GPU Training

Community-contributed. Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc.

Overview

Run distributed GPU training on CoreWeave: single-node multi-GPU and multi-node training with PyTorch DDP, Slurm-on-Kubernetes, and shared storage.

Prerequisites

• CKS cluster with multi-GPU node pools (8xA100 or 8xH100)
• Shared storage (CoreWeave PVC or NFS)
• Training container with PyTorch and NCCL

Instructions

Step 1: Single-Node Multi-GPU Training

# training-job.yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: llm-finetune
spec:
  template:
    spec:
      restartPolicy: Never
      containers:
        - name: trainer
          image: ghcr.io/myorg/trainer:latest
          command: ["torchrun"]
          args:
            - "--nproc_per_node=8"
            - "train.py"
            - "--model_name=meta-llama/Llama-3.1-8B"
            - "--batch_size=4"
            - "--epochs=3"
          resources:
            limits:
              nvidia.com/gpu: "8"
              memory: 512Gi
              cpu: 
          
              
               
              
               
      
          
          
             
          
          
             
      
        
          
            
               
                    
                     
                     []